In silico screening for candidate chassis strains of free fatty acid-producing cyanobacteria
AuthorsMotwalli, Olaa Amin
Jankovic, Boris R.
Ansari, Hifzur Rahman
Arold, Stefan T.
Archer, John A.C.
Bajic, Vladimir B.
KAUST DepartmentApplied Mathematics and Computational Science Program
Bio-Ontology Research Group (BORG)
Biological and Environmental Sciences and Engineering (BESE) Division
Computational Bioscience Research Center (CBRC)
Computer Science Program
Computer, Electrical and Mathematical Sciences and Engineering (CEMSE) Division
Pathogen Genomics Laboratory
SABIC - Corporate Research and Innovation Center (CRI) at KAUST
Structural Biology and Engineering
Structural and Functional Bioinformatics Group
Online Publication Date2017-01-05
Print Publication Date2017-12
Permanent link to this recordhttp://hdl.handle.net/10754/622638
MetadataShow full item record
AbstractBackground Finding a source from which high-energy-density biofuels can be derived at an industrial scale has become an urgent challenge for renewable energy production. Some microorganisms can produce free fatty acids (FFA) as precursors towards such high-energy-density biofuels. In particular, photosynthetic cyanobacteria are capable of directly converting carbon dioxide into FFA. However, current engineered strains need several rounds of engineering to reach the level of production of FFA to be commercially viable; thus new chassis strains that require less engineering are needed. Although more than 120 cyanobacterial genomes are sequenced, the natural potential of these strains for FFA production and excretion has not been systematically estimated. Results Here we present the FFA SC (FFASC), an in silico screening method that evaluates the potential for FFA production and excretion of cyanobacterial strains based on their proteomes. A literature search allowed for the compilation of 64 proteins, most of which influence FFA production and a few of which affect FFA excretion. The proteins are classified into 49 orthologous groups (OGs) that helped create rules used in the scoring/ranking of algorithms developed to estimate the potential for FFA production and excretion of an organism. Among 125 cyanobacterial strains, FFASC identified 20 candidate chassis strains that rank in their FFA producing and excreting potential above the specifically engineered reference strain, Synechococcus sp. PCC 7002. We further show that the top ranked cyanobacterial strains are unicellular and primarily include Prochlorococcus (order Prochlorales) and marine Synechococcus (order Chroococcales) that cluster phylogenetically. Moreover, two principal categories of enzymes were shown to influence FFA production the most: those ensuring precursor availability for the biosynthesis of lipids, and those involved in handling the oxidative stress associated to FFA synthesis. Conclusion To our knowledge FFASC is the first in silico method to screen cyanobacteria proteomes for their potential to produce and excrete FFA, as well as the first attempt to parameterize the criteria derived from genetic characteristics that are favorable/non-favorable for this purpose. Thus, FFASC helps focus experimental evaluation only on the most promising cyanobacteria.
CitationMotwalli O, Essack M, Jankovic BR, Ji B, Liu X, et al. (2017) In silico screening for candidate chassis strains of free fatty acid-producing cyanobacteria. BMC Genomics 18. Available: http://dx.doi.org/10.1186/s12864-016-3389-4.
SponsorsThis publication is based upon work supported by the King Abdullah University of Science and Technology (KAUST) Office of Sponsored Research (OSR) under Awards No URF/1/1976-02 and FCS/1/2448-01.
RelationsIs Supplemented By:
Olaa Motwalli, Magbubah Essack, Jankovic, B., Boyang Ji, Xinyao Liu, Hifzur Ansari, … Bajic, V. (2017). In silico screening for candidate chassis strains of free fatty acid-producing cyanobacteria. Figshare. https://doi.org/10.6084/m9.figshare.c.3658880. DOI: 10.6084/m9.figshare.c.3658880 HANDLE: 10754/624143
CollectionsArticles; Bio-Ontology Research Group (BORG); Biological and Environmental Sciences and Engineering (BESE) Division; Bioscience Program; Applied Mathematics and Computational Science Program; Structural and Functional Bioinformatics Group; Computer Science Program; Computational Bioscience Research Center (CBRC); Computer, Electrical and Mathematical Sciences and Engineering (CEMSE) Division
Except where otherwise noted, this item's license is described as This article is distributed under the terms of the Creative Commons Attribution 4.0 International License (http://creativecommons.org/licenses/by/4.0/), which permits unrestricted use, distribution, and reproduction in any medium, provided you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license, and indicate if changes were made. The Creative Commons Public Domain Dedication waiver (http://creativecommons.org/publicdomain/zero/1.0/) applies to the data made available in this article, unless otherwise stated.